Food steganography

ABSTRACT

The present disclosure relates to methods and systems for calculating a food additive. A first method includes identifying chemical compounds of an averse food ingredient, identifying chemical compounds of a flavorful food ingredient and calculating a set of chemical compounds for the food additive such that an olfactory perception of a mixture of the averse food ingredient, the flavorful food ingredient and the food additive is the same as an olfactory perception of only the flavorful food ingredient. A first device includes a database storing information identifying chemical compounds of an averse food ingredient and identifying chemical compounds of a flavorful food ingredient, and a processor for calculating a food additive such that an olfactory perception of flavors of a mixture of the averse food ingredient, the flavorful food ingredient and the food additive is the same as an olfactory perception of only the flavorful food ingredient.

FIELD OF THE DISCLOSURE

This disclosure relates generally to the field of food preparation, andmore specifically to altering the flavor perception of food usingflavoring compounds.

BACKGROUND OF THE DISCLOSURE

Human flavor perception is complicated, involving a variety of externalsensory stimuli and internal states. Not only does it involve the fiveclassical senses, but also sensing through the gut, and the emotional,memory-related, motivational, and linguistic aspects of food. First,there are the basic tastes: sweet, sour, salty, bitter, and umami. Thesmell of foods is the key contributor to flavor perception, which is inturn a property of the chemical compounds contained in the ingredients.There are typically tens to hundreds of different flavor compounds perfood ingredient.

In addition, many children, as well as adults, are picky eaters. Mostchildren eat a wide variety of foods until they are around two yearsold, when they suddenly stop. The phase can last until a child is fouror five years of age. It is believed to be an evolutionary response.Toddlers' taste buds shut down at about the time they start walking,giving them more control over what they eat. However, junk food such asice cream, French fries and soda are often more attractive to the eaterthan healthy foods such as brown rice and broccoli. This is alsobelieved to be an evolutionary instinct useful when humans used towander around in the woods searching for food. In the distant past,humans depended heavily on their senses to make a decision of what toeat and what not to eat. In nature, foods that are sweet are almostalways safe to eat and are nutritious. They make hunger go away; foodsthat smell odd, or taste bitter or sour usually mean they arepotentially toxic or spoiled, and less safe to eat. In the modernenvironment, where food is bought in supermarkets or restaurants, thosesame survival instincts often serve instead to make humans obese andchronically ill.

SUMMARY OF THE DISCLOSURE

In one embodiment, the present disclosure is a method for calculating afood additive. For example, the method includes identifying chemicalcompounds of an averse food ingredient, identifying chemical compoundsof a flavorful food ingredient and calculating a set of chemicalcompounds for the food additive such that an olfactory perception of amixture of the averse food ingredient, the flavorful food ingredient andthe food additive is the same as an olfactory perception of only theflavorful food ingredient.

In another embodiment, the present disclosure is an additional methodfor calculating a food additive. The method includes identifyingchemical compounds of an averse food ingredient, identifying chemicalcompounds of a flavorful food ingredient, determining a set ofphysicochemical properties of the chemical compounds of the averse foodingredient and calculating physicochemical properties for the foodadditive such that an olfactory perception of a mixture of the aversefood ingredient, the flavorful food ingredient and the food additive isthe same as an olfactory perception of only the flavorful foodingredient. The method further includes selecting chemical compounds forthe food additive, wherein, when the chemical compounds that areselected are mixed to form the food additive, the food additivecomprises the physicochemical properties that are calculated for thefood additive.

In another embodiment, the present disclosure is a further method forcalculating a food additive. The method includes generating a projectionof a combination of a flavorful food ingredient, an averse foodingredient and a food additive in a perceptual space, wherein theprojection comprises a first vector in the perceptual space. The methodnext includes generating a projection of the flavorful food ingredientin the perceptual space, wherein the projection of the flavorful foodingredient in the perceptual space comprises a second vector in theperceptual space. The method then selects the food additive such that adifference between the first vector and second vector is minimized.

In another embodiment, the present disclosure is a device that includesa database storing information identifying chemical compounds of anaverse food ingredient and identifying chemical compounds of a flavorfulfood ingredient, and a processor for calculating a food additive suchthat an olfactory perception of flavors of a mixture of the averse foodingredient, the flavorful food ingredient and the food additive is thesame as an olfactory perception of only the flavorful food ingredient.

In another embodiment, the present disclosure is a system that includesa gas chromatography apparatus, a processor and a compound mixer. Thegas chromatography apparatus is for identifying chemical compounds of anaverse food ingredient and for identifying chemical compounds of aflavorful food ingredient. The processor is for calculating a foodadditive such that an olfactory perception of a mixture of the aversefood ingredient, the flavorful food ingredient and the food additive isthe same as an olfactory perception of only the flavorful foodingredient. The compound mixer is for mixing flavor compounds to createthe food additive.

In still another embodiment, the present disclosure is a device thatinclude a processor and a computer-readable medium storing instructions,which when executed by the processor, cause the processor to performoperations. The operations include identifying chemical compounds of anaverse food ingredient, identifying chemical compounds of a flavorfulfood ingredient and calculating a set of chemical compounds for the foodadditive such that an olfactory perception of a mixture of the aversefood ingredient, the flavorful food ingredient and the food additive isthe same as an olfactory perception of only the flavorful foodingredient.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates a conceptual representation of an embodiment of thepresent disclosure;

FIG. 2 illustrates a graph of olfactory/flavor physicochemical spacereduced to two principal component dimensions, according to the presentdisclosure;

FIG. 3 is a high-level block diagram of an exemplary system forcalculating and producing a food additive, according to the presentdisclosure;

FIG. 4 is a flow diagram of an exemplary method for calculating a foodadditive, according to the present disclosure; and

FIG. 5 is a high-level block diagram of a general purpose computingdevice suitable for use in performing the functions described herein.

DETAILED DESCRIPTION

The present disclosure is directed to several problems relating to thepalatability of nutritious foods. For example, some people and animalsare averse to the flavor of certain foods that have nutritional benefit.It is much easier to get those people or animals to eat foods they findflavorful. If an averse food (e.g., a nutritious food) can be hiddeninside a flavorful food, then it will be easier to feed those people oranimals with those foods. However, human flavor perception iscomplicated, involving a variety of external sensory stimuli andinternal states. For example, the smell of foods is the key contributorto flavor perception, which is in turn a property of the chemicalcompounds contained in the ingredient. However, there are typically tensto hundreds of different chemical compounds contributing to flavor andsmell per food ingredient.

Embodiments of the present disclosure aim to increase the palatabilityof an averse food (that is, a nutritious food that may have a generallyundesirable flavor perception) by using the concept of “flavorwhiteness” to actively sense and estimate the flavor composition of theaverse food and at least one flavorful food, and then to find an optimalset of flavor compounds to produce a food additive to mix with the twofoods such that only the flavor of the flavorful food is perceived whenconsumed in a mixture. Conceptually, the present disclosure can bedescribed as a food steganography process that hides the flavor of anunpleasant or averse food.

Steganography is the science of hiding information. Whereas the goal ofcryptography is to make data unreadable by a third party, the goal ofsteganography is to hide the data. There are a large number of familiarsteganographic methods: invisible ink and microdots, secreting a hiddenmessage in the second letter of each word of a large body of text,spread spectrum radio communication, etc. The following formula providesa generic description of the steganographic process:cover_medium+hidden_data+stego_key=stego_medium. The process of thepresent disclosure comprises using a flavorful food as the cover medium,the averse food as the hidden data and the food additive that isproduced as the steganographic key, which are combined to produce aresulting food composition, the “steganographic medium.” FIG. 1,illustrates a conceptual representation 100 of an embodiment the presentdisclosure. In particular, representation 100 illustrates the combiningof a flavorful food 110 (e.g., macaroni and cheese) with an averse food120 (e.g., cauliflower) and a calculated food additive 130 such that inthe resulting food dish 140, only the flavorful food is perceived.

Flavor whiteness is a concept relating to flavor perception that hasanalogs within other sensory fields. For example, in the area of visualperception, two mixtures, each containing an independent set of manydifferent wavelengths, may produce a common color percept termed“white.” In the realm of auditory perception, or “audition,” twomixtures, each containing an independent set of many differentfrequencies, may produce “white noise.” Visual and auditory whitesemerge upon two conditions: when the mixture components span stimulusspace, and when they are of equal intensity. Similarly, at least onestudy has shown that similar conditions apply to odorant mixtures whichcan be merged to produce an “olfactory white”. For example, Weiss, etal. in “Perceptual convergence of multi-component mixtures in olfactionimplies an olfactory white,” Proceedings of the National Academy ofSciences of the United States of America, vol. 109, no. 49, pp.19959-19964, Dec. 4, 2012, shows experimentally that mixtures ofapproximately 30 or more olfactory components which have features thatspan the stimulus space and which are of relatively equal intensity leadto a common olfactory perception, which has been termed olfactory white.Notably, mixtures of entirely different components that do not overlapor only partially overlap nevertheless can lead to the same perceptionof olfactory white.

Since the smell of foods is the key contributor to flavor, an olfactorywhite implies that a flavor white also exists. However, the stimulusspace for olfactory perception, and therefore for flavor perception, isfar more complicated than the analogs for visual and auditoryperception. For example, for visual and auditory perception, there arewell defined ranges of frequencies/wavelengths and magnitudes of signalsthat can be perceived by humans. Thus, each of these modes of perceptionessentially comprise a single dimension (frequency/wavelength of lightand frequency/wavelength of sound waves, respectively). On the otherhand, there is not a singular aspect to the perception of smell (andflavor). Rather, olfactory perception and flavor perception includes avast number of dimensions.

For example, the olfactory perceptual space may include ahyper/multidimensional space that may include up to 146 or moreperceptual labels/descriptors (e.g., 146 dimensions) which may includethe well known descriptors: fruity, floral, fragrant, soapy, sweet,sulfurous, yeasty, and so forth. Each one of these descriptors(dimensions) may have a different intensity weight for a different foodingredient and for different common isolated chemical compounds that maybe found in foods and fragrances. Thus, in the perceptual space, eachfood ingredient or chemical compound may be represented as a vector in Xdimensions, where X is a number of available perceptual descriptors,wherein each perceptual descriptor occupies a different dimension, andwhere a value of the vector in each dimension relates to a perceivedintensity of the food ingredient or chemical compound with respect tothat particular descriptor.

It should be noted that the present disclosure may in some instancesdescribe olfactory perceptual and physicochemical spaces, and in otherinstances describe flavor perceptual and physicochemical spaces.However, insofar as smell is the primary contributor to flavor, theolfactory and flavor spaces may be considered interchangeably forpurposes of the present disclosure. In addition, it is noted that morethan 100 common chemical compounds in foods and fragrances have beenquantified in the olfactory perceptual space, e.g., using human testsubjects to rate and quantify perceptions of the isolated chemicalcompounds. In other words, at the very least, these compounds have knownvector representations in the perceptual space. However, there is avastly greater number, more than 1000 known compounds, commonly used forolfaction and flavor research and which are known to contribute to smelland/or flavor perception. As an example, one particular cheese may havemore than 600 of these unique chemical compounds contained therein indifferent amounts. Similarly, cauliflower may have more than 70 uniquecompounds contained therein in different amounts.

In addition, a food and/or chemical compound present in the food can berepresented as a vector based upon one or more physicochemical orphysical descriptors in a physicochemical space. For example, thephysicochemical space may have more than 1500 dimensions relating to themolecular properties of different chemical compounds, of whichapproximately only 40-50 are considered statistically relevant toolfaction and/or flavor. These physicochemical properties include: themolecule's polarity, a number of bonds, a number of hydrogen atoms, anumber of heavy atoms, presence and quantity of esters (e.g.,monoesters, diesters, triesters, etc.), aldehydes and/or ketones, alength of ester sidechain, and so forth. Thus, each chemical compoundcan be represented as a vector in the physicochemical space based uponits molecular properties. For example, a compound with 7 bonds and 5hydrogen atoms and that is a diester may comprise a vector of [7, 5, 2]in the dimensions of “number of bonds” and “number of hydrogen atoms”and “number of ester groups.” It should be noted that the presentdisclosure considers that olfactory perception (and flavor perception)correlates to features of molecules, rather than the identities of themolecules.

In one example, the perceptual space may comprise up to 146 dimensions,while the physicochemical space may comprise more than 1500 dimensions(or between 40 and 50 dimensions if limited to those considered mostrelevant to olfaction). However, each of the physicochemical andperceptual spaces may be collapsed into hyper/multidimensional spaceswith a smaller number of dimensions. For example, any one or moredimensions in the full perceptual space may be collapsed into a lessernumber of “principal component” dimensions, each principal componentdimension including from one to several of the original dimensions. Forexample, a multidimensional space may be collapsed into atwo-dimensional space having a first dimension, principal component 1,and a second dimension, principal component 2. In addition, in principalcomponent analysis (PCA), different dimensions are selected foraggregation with one another such that in the resulting space with areduced number of dimensions, a maximum variability in the data set iscaptured given the available number of dimensions in thereduced-dimensional space. Note that the principal component dimensionsare orthogonal to one another. Thus, the features represented by each ofthe principal component dimensions remain orthogonal to one another.

Vectors in a hyper-dimensional space can similarly be collapsed to alesser-dimensional vector by collapsing each of its constituentcomponents in each of the collapsed dimensions. As just one example,FIG. 2 illustrates an exemplary graph 200 of the physicochemical spacereduced to two principal component dimensions (PC1 and PC2). Forinstance, the points illustrated on the graph 200 may represent theprincipal component vectors of different chemical compounds contained ina flavorful food, such as macaroni and cheese. In one example, thephysicochemical and/or the perceptual space is normed such thatdifferent dimensions, relating to different properties which are notnecessarily of the same type, are scaled accordingly.

With respect to the olfactory perceptual space, it has been shown thatonly two principal component dimensions capture greater than 50 percentof the variance of known chemicals in the original 146-dimensionalspace. With only approximately 10 dimensions, nearly 90 percent of thevariation can be retained. Similarly, it has been shown that the firsttwo principal component dimensions in the physicochemical space mayaccount for more than 40 percent of the entire variance in thephysicochemical space, while the first 10 principal component dimensionscan account for 70 percent of the variation.

In addition to the above, the perceptual space is known to generallycorrespond to the physicochemical space. In other words, attributes inthe perceptual space can be predicted from attributes in thephysicochemical space, and vice versa. In particular, more than 100common compounds have been quantified in the perceptual space. Further,since the molecular properties of these chemicals are also known, ageneralized correspondence between particular physicochemical propertiesin the physicochemical space and perceptual descriptors and magnitudesthe perceptual space has been derived from this rich set of data. Forexample, it has been shown that with a two-dimensional perceptual spaceand a two-dimensional physicochemical space created by principalcomponent analysis (PCA), there is a strong correlation between a firstprincipal component dimension in the perceptual space and a firstprincipal component dimension in the physicochemical space and asimilarly strong correlation between a second principal componentdimension in the perceptual space and a second principal componentdimension in the physicochemical space.

It should be noted that these exemplary principal component dimensionsare not observable in the real world and cannot be experienced andperceived as such. However, to provide some frame of reference, theperceptual descriptors most strongly associated with the first principalcomponent dimension in the perceptual space may include fragrant/sweetat the one extreme and putrid/rancid at the other extreme, while thesecond principal component dimension is associated with the descriptorsof ether/gasoline at one extreme and smoky/woody at the other extreme.Similarly, in the two-dimensional physicochemical space, the firstprincipal component dimension may be most strongly associated withhydrophobicity and polarity while the second principal componentdimension may be strongly associated with a number of carbon atoms,among other physicochemical properties.

The foregoing example describes the correlation between the first twoprincipal component dimensions in the perceptual space and the first twoprincipal component dimensions in the physicochemical space. This isperhaps the most important and the most useful statistical correlationbetween the perceptual and physicochemical spaces. However, it should benoted that the use of further correlations based upon individualphysicochemical properties may also be employed by embodiments of thepresent disclosure. For example, the length of side-chains of dipeptideesters has been correlated to the degree of sweetness. Thus, amathematical correspondence between the ester side-chain length of acompound (e.g., a dimension in the physicochemical space) and thesweetness and/or fruitiness (e.g., a single dimension, or two dimensionsin the perceptual space) may be inferred.

As further isolated chemical components are quantified in the perceptualspace, more accurate correlations between other physicochemicalproperties (physicochemical dimensions) and other perceptual descriptors(perceptual dimensions) can be inferred. The same framework can beextended to derive correspondences between the physicochemical space andthe perceptual space for various additional chemical compounds thatshare similar features with the more than 100 chemical compounds thathave previously been quantified in the perceptual space. In any case, avector representation of a chemical compound in the perceptual space isassociated with an analogous vector representation of the compound inthe physicochemical space whether explicitly (e.g., for the at least 100chemical compounds that have been quantified experimentally in theperceptual space) or by inference as described above (e.g., using theessentially direct correlation between the two-dimensional perceptualspace and the two-dimensional physicochemical space).

To further aid in understanding the present disclosure, FIG. 3illustrates an exemplary system 300 for calculating an optimal foodadditive for steganographic combining with at least one averse food andat least one flavorful food. In particular, the system 300 includes fiveprincipal components: chemical sensor 310, flavor compound and intensityestimator 320, compound mixture optimizer 330, compound mixer 340 andfood additive producer 350. The system 300 accepts as inputs at leastone averse food 361, e.g., cauliflower, and at least one flavorful food362, e.g., macaroni and cheese, which are analyzed by chemical sensor310. It should be noted that as used herein the terms averse food andflavorful food are broadly applicable to different classes and types offoods as they may be perceived by any one or more individuals. Thus, forexample, one person may find a particular food to be undesirable oraverse, while another person may enjoy the same food and consider it tobe desirable, tasty and flavorful. As such, embodiments of the presentdisclosure are broadly applicable to the hiding of one food with anotherfood using a flavor additive that is determined in accordance with thesystems, devices and methods described herein. Accordingly, the term“averse food” is broadly applicable to any food which is to be “hidden,”and the term “flavorful food” is broadly applicable to any food which isused as a cover medium to hide the averse food.

In one embodiment, chemical sensor 310 is for detecting chemicalcomponents of both the at least one averse food 361 and the at least oneflavorful food 362. For example, the chemical sensor/intensity estimator310 may detect more than 70 individual chemical components ofcauliflower and more than 600 individual chemical components of cheese,along with the quantities and/or percentages by weight of each componentwithin each of the foods. In one embodiment, the chemical sensor 310uses gas chromatography, which may include mass-spectrometry,photo-ionization detection, and the like, to determine the componentsthat are present and their overall and/or relative quantities.Accordingly, chemical sensor 310 may comprise or may be part of a gaschromatography apparatus, as is known to those skilled in the art. Itshould be noted that in many instances, well-known foods have alreadybeen profiled in this manner. As such, in one embodiment the system 300may simply obtain a chemical profiles of the at least one averse food361 and/or the at least one flavorful food 362, e.g., as stored data.Thus, in one example, chemical sensor 310 may be considered an optionalpart of the system.

The next component of system 300 is the flavor compound and intensityestimator 320, which determines the chemical compounds that are deemedto most strongly contribute to the olfactory perception and flavor ofthe at least one averse food 361 and the at least one flavorful food362. For instance, as mentioned above, a particular cheese may includemore than 600 different chemical compounds. However, many of the 600chemical compounds may only be present in trace amounts. In addition,many of the 600 chemical compounds may be known to have little or noimpact on flavor (and olfactory) perception, whereas other ones of the600 chemical compounds may be known to have a strong contribution to theflavor (and olfactory) perception. In any case, the flavor compound andintensity estimator 320 may obtain this information from the chemicalsensor 310 and/or from stored data relating to common foods.

As also mentioned above, each chemical compound of a food ingredient canbe represented in the physicochemical space as a vector based directlyupon known physical and/or chemical features of the chemical compound,e.g., number of bonds, number of carbon atoms, hydrophobicity, number ofhydrogen atoms, length of ester sidechains, quantities of ester groups,alkyl groups and ketone groups, and so forth. As such, the flavorcompound and intensity estimator 320 may select the top X and/or Ychemical compounds of the at least one averse food 361 and/or the atleast one flavorful food 362 and determine their physicochemicalvectors. From these selected sets of vectors in the physicochemicalspace, the flavor compound and intensity estimator 320 may thentranslate or project the selected set of vectors for each of the atleast one averse food 361 and/or the at least one flavorful food 362 torespective vectors in the flavor perceptual space.

It should be noted that in another embodiment, a projection of a food(in aggregate) may be known. For example, flavor/olfactory perceptionscores for macaroni and cheese (as opposed to flavor perception scoresfor the pure, individual chemical compounds contained therein) may havebeen created or may be created via flavor perception surveys. In thiscase, a vector in the perceptual space for macaroni and cheese need notbe derived via projection of its constituent chemicals from thephysicochemical space to the perceptual space.

Compound mixture optimizer 330 is tasked with calculating an optimalfood additive 371 to be added with the at least one averse food 361 andthe at least one flavorful food 362 such that when a food dishcomprising the mixture of the three is consumed, the person eating themixture will only perceive the flavorful food. Accordingly, the compoundmixture optimizer performs such calculations over the flavor perceptualspace. However, this task may lead to an unbounded solution set (anessentially infinite number of solutions). As such, compound mixtureoptimizer 330 calculates at least one optimal solution to the problem.For instance, in one embodiment, the optimizer 300 may find a “lowestcost” or a low cost solution. In one embodiment, the lowest costsolution may be constrained by the availability or non-availability ofcertain compounds, the cost of such compounds, the relative nutrition orlack thereof of certain compounds, and so forth.

Once the optimal food additive 371 is calculated, compound mixer 340 mayobtain and mix the desired quantities of the compounds that arecalculated. In addition, food additive producer 350 may further processthe mixture to place it in a form that is suitable for inclusion in foodand/or for human consumption. It should be noted that in one example,the flavor compound and intensity estimator 320 and compound mixtureoptimizer 330 may comprise a single device rather than separate devicesor modules. As an example, the flavor compound and intensity estimator320 and compound mixture optimizer 330 may be embodied as a singlecomputing device, such as general purpose computing device 500 of FIG.5.

In one embodiment, the calculations made by compound mixture optimizer330 may be implemented as follows:

₊ ^(n) represents a non-negative real space of flavor compoundquantities. For example, n is the number of available chemical compounds(there are more than 1000 known and typically used for flavor/olfactoryresearch).

g, h, a∈

₊ ^(n) are flavor compound vectors for the flavorful food, the aversefood and the optimal compound mixture respectively. For example, g is avector comprising the chemical compounds and the amounts present in theflavorful food, h is a vector comprising the chemical compounds and theamounts present in the flavorful food and a is a vector comprised of aplurality of chemical compounds to be computed and their amounts.

P is a projection from

₊ ^(n) to

^(d) where d<n such that

^(d) is a normed flavor perceptual space of d dimensions. It should benoted that g, h and a can be mapped into the physicochemical space asrespective sets of vectors in the physicochemical space, which may thenbe mapped into the perceptual space as respective vectors in theperceptual space.

w∈

represents olfactory (and flavor) white (it is a vector in theperceptual space).

One way to represent the optimization problem for the compound mixtureoptimizer 330 is:

$\begin{matrix}{\min\limits_{a}{{{P\left( {g + h + a} \right)} - {P(g)} - w}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

where P(g+h+a) is a vector in the perceptual space that corresponds tothe set of vectors in the physicochemical space for the collectivechemical components from all of the flavorful food, the averse food andthe flavor additive. P(g) is a vector in the perceptual space thatcorresponds to the perception of the flavorful food alone (e.g.,macaroni and cheese). Thus, P(g) may be projected from the set ofvectors representing macroni and cheese in the physicochemical space.

It is noted that the minimization problem of Equation 1 has an unboundedsolution set. Thus, the problem can be restated as:

$\begin{matrix}{{\min\limits_{a}{{{P\left( {g + h + a} \right)} - {P(g)} - w}}} + {\lambda \; {J(a)}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

where λ is a scalar and J(a) is a regularization term that is a functionof vector a and which may incorporate objectives such as finding thesolution with the minimum norm (including sparsity), minimum monetarycost, maximizing lifetime of compound mixer 340 by evenly usingavailable constituent chemicals, minimizing the use of certain types ofchemical compounds (e.g., minimizing the inclusion of alcohols,artificial flavors and the like), minimizing the quantity or size ofcompounds to be added, and other optimality principles.

It should be noted that in Equations 1 and 2, the problem attempts tofind a vector a such that P(g+h+a) is as close to P(g) as possible. Inother words, select a set of compounds and select the amounts of eachcompound to mix into a food additive such that the perception of a fooddish comprising the flavorful food, the averse food and the foodadditive tastes and/or smells as close to the taste/flavor of theflavorful food as possible. Note that the optimization problem includesa perceptual space vector for olfactory flavor white, vector w. The needto include w in the optimization problem arises from the fact thatflavor and smell are not subtractive. In other words, there are noflavors or smells which negate each other in the sense that some soundor light waves may cancel each other out to form a null or zero. Thus,olfactory white is not a true “zero” point, but has some positiveperception. As such, w is included in the optimization problem as anormalization term.

It is also noted, however, that there is not a single olfactory/flavorwhite. Instead, many different combinations of approximately 30 orgreater chemical compounds having features that span the stimulus spaceand which are of relatively equal intensity can be considered to beolfactory/flavor white. Accordingly, in one example the projection ofthese different olfactory/flavor white chemical compound combinations inthe perceptual space may be set as: W⊂

^(d). In this example, the optimization problem becomes:

$\begin{matrix}{{\min\limits_{a}{\min\limits_{w \in W}{{{P\left( {g + h + a} \right)} - {P(g)} - w}}}} + {\lambda \; {J(a)}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

where the goal is to find a vector, a, such that P(g+h+a) is as close toP(g) as possible, but where a range of different possibleolfactory/flavor whites w∈W, may be used. For instance, any combinationof approximately 30 or more chemical compounds having features that spanthe feature space and which have relatively equal intensities comprisesan olfactory white.

FIG. 4 is a flow diagram illustrating one embodiment of a method 400 forcalculating a food additive. More specifically, the method 400 is forsteganographic combining of an averse food, a flavorful food and anoptimal food additive comprising an mixture of flavor compounds suchthat when the combination is consumed, only the flavor and/or smell ofthe flavorful food is perceived. In one example, any one or more steps,functions and/or operations of the method 400 may be implemented by anyone or more components of the system 300 in FIG. 3. Alternatively, or inaddition, any one or more steps, functions and/or operations of themethod 400 by may be performed by a computing device 500 and/or aprocessor of such a computing device as described in connection withFIG. 5 below. For illustrative purposes, the method 400 is describedbelow in connection with this particular example.

The method 400 begins at step 402 and proceeds to step 410 where theprocessor identifies chemical compounds of at least one averse food(broadly, an averse food ingredient) and their quantities. For example,in one embodiment a gas chromatography apparatus may be used to sensethe chemical compounds that constitute the at least one averse food andto determine the weights and/or relative amounts present in the at leastone averse food. more than 70 unique chemical compounds may be found incauliflower in this manner. In another embodiment, at step 410 theprocessor obtains data regarding the chemical compounds and theiramounts present in the at least one averse food from a database/datastore, e.g., from any one or more of an attached memory, an externaldatabase accessed over a network, a disk drive, and the like. Forinstance, a database may be embodiment in a storage device, such asstorage device 506 in FIG. 5, discussed below.

In step 420, the processor identifies chemical compounds of at least oneflavorful food (broadly, at least one flavorful food ingredient) andtheir quantities. In one embodiment, a gas chromatography apparatus maybe used to sense the chemical compounds of the at least one flavorfulfood and to determine their relative weights and/or relative amountspresent in the at least one flavorful food. For example, gaschromatography may determine that a flavorful food such as macaroni andcheese is composed of more than 600 unique chemical compounds. Inanother embodiment, at step 420 the processor obtains data regarding thechemical compounds and their amounts present in the at least oneflavorful food from a data store, e.g., from any one or more of anattached memory, an external database accessed over a network, a diskdrive, and the like.

In step 430, the processor determines a set of physicochemicalproperties of a plurality of chemical compounds of the at least oneaverse food and a plurality of chemical compounds of at least oneflavorful food. For instance, as described above, cauliflower (an aversefood in this example) and macaroni and cheese (a flavorful food in thisexample) may be comprised of more than 70 and 600 chemical compoundsrespectively. However, although the number of chemical compounds presentmay be relatively large, it is by and large known which compoundscontribute to smell and flavor and which have little effect in theolfactory/flavor perceptual space. As such, certain compounds may beexcluded from further analysis and processing at step 430, allowing theprocessor to focus on only the most important compounds. In addition,certain compounds may be present in such trace amounts that they mayalso be ignored and/or excluded from further processing at step 430,since these compounds also contribute little to the overall flavor andsmell of the food. Accordingly, step 430 may involve selecting only aportion of the chemical compounds present in either or both of the atleast one averse food and the at least one flavorful food.

In any case, once the chemical compounds of the at least one averse foodand the at least one flavorful food are known, relevant physicochemicalproperties of each of the compounds may readily be determined. Forexample, there are more than 1000 well known chemical compounds that areused in olfactory and flavor research and which are known to contributeto smell and/or flavor. In addition, the physicochemical properties ofthese compounds are also well known such that, once a particularchemical compound is identified, it can be correlated to itsphysicochemical properties. For example, the identities of flavorcompounds and their physicochemical properties may be stored in a listor database such that the properties can simply be looked-up and indexedby chemical compound name/identity.

In one embodiment, at step 430 the processor may create a vector in thephysicochemical space for each of a plurality of chemical compoundsselected for either or both of the at least one averse food and the atleast one flavorful food. For instance, the processor may determine avector for each compound in a multi-dimensional physicochemical space,where each dimension corresponds to a particular physicochemicalproperty. Alternatively, or in addition, the processor may create avector for each chemical compound in a lesser dimensional, “normed”space having principal component dimensions which may comprise linearprojections of two or more dimensions aggregated with one another,wherein each principal component dimension is orthogonal to the otherprincipal component dimensions, and where the principal componentdimensions are selected to maintain a maximum variance in the data setgiven the resulting number of dimensions in the reduced-dimensionalspace. In any case, at step 430 the processor may create in thephysicochemical space a set of vectors for the plurality of chemicalcompounds of the averse food and a set of vectors for the plurality ofchemical compounds flavorful food.

At step 440, the method 400 calculates a food additive such that whenthe at least one flavorful food, the at least one averse food and theflavor additive are mixed into a single food dish and consumed, only theflavorful food is perceived. For example, the processor may solve anoptimization problem, e.g., based upon Equations 1, 2 or 3 above. Forinstance, the processor may determine a minimum cost vector comprising aplurality of flavor compounds and their relative quantities, using anyof these equations. In one embodiment, the optimization problem involvesfinding a vector a such that P(g+h+a) is as close to P(g) as possible,where P(g) is a projection (vector) in the perceptual space representingthe perception of the at least one flavorful food and P(g+h+a) is aprojection (vector) in the perceptual space representing theaggregate/combined perception of the at least of flavorful food, g, theat least one averse food, h, and the flavor additive, a, mixed togetherin one food dish.

In one embodiment, vector a is calculated based upon a correlationbetween the perceptual space and the physicochemical space. In otherwords, desirable perceptual qualities and relative intensities that arenecessary to bring P(g+h+a) into similarity P(g) are determined in theperceptual space, which are then capable of reverse-projection intodesirable physicochemical properties in the physicochemical space. Forexample, with a two-dimensional perceptual space and a two-dimensionalphysicochemical space created by principal component analysis (PCA),there is a strong correlation between a first principal componentdimension in the perceptual space and a first principal componentdimension in the physicochemical space and a similarly strongcorrelation between a second principal component dimensions in theperceptual space and a second principal component dimension in thephysicochemical space. Accordingly, at step 440 these and other knownconnections between perceptual descriptors and intensities andphysicochemical properties may be used to reverse-project into thephysicochemical space. In other words, desirable physicochemicalproperties (which may be represented by a vector in the physicochemicalspace) maybe identified by reverse-projecting from the perceptual spaceinto the physicochemical space.

Accordingly, in one embodiment, at step 440, the processor calculates afood additive comprising a set of flavor compounds such that when mixedtogether, the food additive has these desirable physicochemicalproperties. It is noted that as stated, this task has practically aninfinite number of solutions. For instance, as mentioned above adatabase may include a list of known chemical compounds and theirphysicochemical properties, such that chemical compounds that satisfythe criteria of the optimization problem can be determined by matchingdesirable physicochemical properties of the food additive determined atstep 440 with physicochemical properties of the different chemicalcompounds listed in the database. However, in the realm of availablechemical compounds there exist numerous different individual chemicalcompounds which may satisfy one or more aspects of the particularphysicochemical criteria determined at step 440. Thus, in one example,at step 440 the processor further calculates the food additive undercertain constraints, such as minimizing the cost of the food additivebased upon the costs of the different available chemical components orcalculating the food additive components where certain chemicalcompounds may not be available or may be disfavored (e.g., alcohols,artificial versus naturally derived chemical components, and so forth).In one example, these optimization criteria/constraints are representedby the term λJ(a) in Equations 2 and 3 above.

In one embodiment, at step 440, the processor further performs thecalculation of the food additive while normalizing the optimizationproblem to an olfactory white. It is noted that olfactory white ariseswhen approximately 30 or more chemical compounds are present and havefeatures spanning the stimulus space with relatively equal intensity.Since olfactory white is anon-zero concept, the minimization of thedistance between the projection of the at least one flavorful food andthe projection of the aggregate food dish (the combination of the atleast of flavorful food, the at least one averse food and the flavoradditive mixed together in one food dish) is normalized to the vector ofan olfactory white in the perceptual space.

It is also noted that there are many possible olfactory whites. Thus, inone embodiment step 440 involves finding a lowest cost solution usingany available olfactory white (e.g., a lowest cost and/orclosest-distance olfactory white). In other words, the particular lowestcost solution that is found at step 440 may be affected by externalcriteria, such as described above, e.g. λJ(a), which may comprise apreference for naturally derived flavor compounds, or less expensive ormore readily available flavor compounds, as well as by the distance to aclosest olfactory white vector. This particular example is captured inEquation 3 above.

In one embodiment, at step 440 the processor further outputs thecomposition of the food additive that is calculated. For example, theprocessor may output a recommendation for a food additive comprising amixture of any number of different chemical compounds (e.g., 5-15different compounds). It is noted that olfactory white arises whenapproximately 30 or more chemical compounds are present and havefeatures spanning the stimulus space with relatively equal intensity.However, since the food additive is being combined with the compoundsthat are already present in the at least one averse food and the atleast one flavorful food, the necessary spanning of the stimulus spacewill often be achievable with a food additive having substantially lessthan 30 chemical compounds.

Following step 440, the method 400 proceeds to step 495 where the methodends.

It should be noted that in various embodiments, any one or more of steps410-440 of the method 400 may be performed in a different order thatthat which is illustrated herein. Similarly, any one or more of steps410-440 may be considered to be optional steps, and may therefore beomitted without departing from the scope of the present disclosure. Inaddition, although not expressly specified above, one or more steps,functions or operations of method 400 may include a storing, displayingand/or outputting step as required for a particular application. Inother words, any data, records, fields, and/or intermediate resultsdiscussed in the method can be stored, displayed and/or outputted toanother device as required for a particular application. Furthermore,steps or blocks in FIG. 4 that recite a determining operation or involvea decision do not necessarily require that both branches of thedetermining operation be practiced. In other words, one of the branchesof the determining operation can be deemed as an optional step.

FIG. 5 is a high-level block diagram of a general purpose computingdevice 500 suitable for use in performing the steps, functions andoperations described herein. In one embodiment, general purposecomputing device 500 comprises a processor 502, a memory 504, a module505 for calculating a food additive and various input/output (I/O)devices 506 such as a display, a keyboard, a mouse, a stylus, a wirelessnetwork access card, an Ethernet interface, and the like. In oneembodiment, at least one I/O device is a storage device (e.g., a diskdrive, an optical disk drive, a floppy disk drive). It should beunderstood that the module 405 can be implemented as a physical deviceor subsystem that is coupled to a processor through a communicationchannel.

Alternatively, the module 405 for calculating a food additive can berepresented by one or more software applications (or even a combinationof software and hardware, e.g., using Application Specific IntegratedCircuits (ASIC)), where the software is loaded from a storage medium(e.g., I/O devices 506) and operated by the processor 502 in the memory504 of the general purpose computing device 500. Thus, in oneembodiment, the module 505 for calculating a food additive, as describedherein with reference to the preceding figures, can be stored on atangible (e.g., non-transitory) computer readable storage medium (e.g.,RAM, magnetic or optical drive or diskette, and the like).

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof. Various embodiments presentedherein, or portions thereof, may be combined to create furtherembodiments.

What is claimed is:
 1. A method, comprising: identifying a first set ofchemical compounds contained in an averse food ingredient; identifying asecond set of chemical compounds contained in a flavorful foodingredient, different from the averse food ingredient; formulating anew, third set of chemical compounds, different from the first set ofchemical compounds and the second set of chemical compounds, byselecting individual chemical compounds to combine in the third set ofchemical compounds and by selecting defined quantities for each of theindividual chemical compounds, wherein when the individual chemicalcompounds are mixed in the defined quantities, they produce a foodadditive such that an olfactory perception of a mixture of the aversefood ingredient, the flavorful food ingredient and the food additive isthe same as an olfactory perception of only the flavorful foodingredient.
 2. The method of claim 1, wherein the formulating comprises:minimizing a difference between a vector projection of the mixture ofthe averse food ingredient, the flavorful food ingredient, and the foodadditive in a perceptual space with a vector projection of the flavorfulfood in the perceptual space.
 3. The method of claim 2, wherein theminimizing the difference comprises normalizing a solution to a vectorrepresentation of an olfactory white in the perceptual space.
 4. Themethod of claim 2, wherein the perceptual space comprises amultidimensional space where each dimension is associated with adifferent olfactory or flavor perceptual descriptor.
 5. The method ofclaim 2, the perceptual space comprises a multidimensional space whereat least one dimension is associated with at least two differentolfactory or flavor perceptual descriptors.
 6. The method of claim 2,wherein the formulating comprises determining a set of physicochemicalproperties for the food additive, where the determining the set ofphysicochemical properties for the food additive is based upon at leastone correlation between the perceptual space and a physicochemicalspace.
 7. The method of claim 1, wherein the formulating comprises:calculating a set of physicochemical properties for the food additivesuch that the olfactory perception of the mixture of the averse foodingredient, the flavorful food ingredient, and the food additive is thesame as the olfactory perception of only the flavorful food ingredient.8. The method of claim 7, wherein the individual chemical compounds areselected such that, when the individual chemical compounds are combinedin the defined quantities, the food additive comprises the set ofphysicochemical properties that is calculated for the food additive. 9.The method of claim 1, wherein the identifying the first set of chemicalcompounds of the averse food ingredient comprises sensing chemicalcompounds in the averse food ingredient by gas chromatography, andwherein the identifying the second set of chemical compounds of theflavorful food ingredient comprises sensing chemical compounds in theflavorful food by gas chromatography.
 10. The method of claim 1, whereinthe individual chemical compounds in the third set of chemical compoundsare selected based at least in part upon an availability of one or moreof the individual chemical compounds in the third set of chemicalcompounds or such that a cost of the third set of chemical compounds isminimized.
 11. The method of claim 1, further comprising: determining afirst set of physicochemical properties of the first set of chemicalcompounds of the averse food ingredient; and determining a second set ofphysicochemical properties of the second set of chemical compounds ofthe flavorful food ingredient.
 12. The method of claim 11, wherein thedetermining the first set of physicochemical properties of the first setof chemical compounds of the averse food ingredient comprises performinga database lookup to match the first set of chemical compounds of theaverse food ingredient with their respective physicochemical properties,and wherein the determining the second set of physicochemical propertiesof the second set of chemical compounds of the flavorful food ingredientcomprises performing a database lookup to match the second set ofchemical compounds of the flavorful food ingredient with theirrespective physicochemical properties.
 13. The method of claim 11,further comprising: determining a first set of vectors in aphysicochemical space based upon the first set of physicochemicalproperties of the first set of chemical compounds of the averse foodingredient; and determining a second set of vectors in thephysicochemical space based upon the second set of physicochemicalproperties of the second set of chemical compounds of the flavorful foodingredient.
 14. The method of claim 13, wherein the physicochemicalspace comprises a multidimensional space where each dimensions isassociated with a different physicochemical property.
 15. The method ofclaim 13, wherein the physicochemical space comprises a normedmultidimensional space where at least one dimension is associated withat least two different physicochemical properties.
 16. The method ofclaim 13, further comprising: calculating a first vector projectionassociated with the mixture of the averse food ingredient, the flavorfulfood ingredient, and the food additive in a perceptual space, whereinthe calculating the first vector projection is based on the first set ofvectors in the physicochemical space and the second set of vectors inthe physicochemical space; and calculating a second vector projectionassociated with the flavorful food in the perceptual space, wherein thesecond vector projection is based on the second set of vectors in thephysicochemical space.
 17. The method of claim 1, further comprising:obtaining the individual chemical compounds in the third set of chemicalcompounds in the defined quantities; and mixing the individual chemicalcompounds in the defined quantities to produce the food additive in aform that is suitable for human consumption.
 18. A method, comprising:identifying a first set of chemical compounds contained in an aversefood ingredient; identifying a second set of chemical compoundscontained in a flavorful food ingredient; determining a first set ofphysicochemical properties of the first set of chemical compounds of theaverse food ingredient; determining a second set of physicochemicalproperties of the second set of chemical compounds of the flavorful foodingredient; calculating a third set of target physicochemical propertiesfor a food additive such that an olfactory perception of a mixture ofthe averse food ingredient, the flavorful food ingredient, and the foodadditive is the same as an olfactory perception of only the flavorfulfood ingredient; and formulating the food additive by selectingindividual chemical compounds to include in a new, third set of chemicalcompounds, different from the first set of chemical compounds and thesecond set of chemical compounds, and by selecting defined quantities inwhich to mix the individual chemical compounds in the third set ofchemical compounds, wherein when the individual chemical compounds aremixed in the defined quantities, they produce a food additive whosephysicochemical properties match the third set of target physicochemicalproperties.
 19. The method of claim 18, wherein the calculating thethird set of target physicochemical properties for the food additivecomprises: minimizing a difference between a vector projection of themixture of the averse food ingredient, the flavorful food ingredient,and the food additive in a perceptual space with a vector projection ofthe flavorful food in the perceptual space.
 20. The method of claim 19,wherein the minimizing the difference comprises normalizing a solutionto a vector representation of an olfactory white in the perceptualspace.